In January 2017, Ornat Turin, a researcher at the Gordon College of Education in Haifa, Israel, analyzed the common features of education in popular films and literary works of science fiction. Turin began by observing that:
The school, as an ultra-conservative institution, stands in stark contrast to the bold and innovative characteristics of science fiction. Many elements that emerged during the industrial revolution are still present: the blackboard, the chairs, the bell and the teacher-pupil hierarchy.
Sadly, she concluded that not even in science fiction are we aware of an alternative educational system to the traditional one:
The more pleasant aspects of futuristic learning involve choice: choice between right and wrong and choices of interests and topics. The learning process will be independent, motivated by fascination, a desire to experiment and the will to extend wisdom.
What follows is an informal description of the Decentralized Autonomous Education (DAE) project, which proposes an innovative learning model based on the principles and applications of Web3. This model is realistic and effective, venturing well beyond the traditional education system without straying into science fiction.
DAE is a gamification of the learning process. It models the class as a Decentralized Autonomous Organization (DAO).
Imagine a game whose characters are a teacher, a group of (at least two) students, and a topic known by the teacher but not by the students. The purpose of the game is to maximize the transfer of knowledge about the topic from the teacher to the students in a homogeneous way, i.e. without disparities.
To play the learning game one needs first to identify the stakeholders — those who depend on the learning process achieving its goal in the best way possible. Of course, the teachers and students of the class will hold a stake. But further stakeholders might be companies interested in hiring the students in the future, or indeed teachers of parallel courses.
A crucial feature of the learning game is karma. Each stakeholder’s karma varies over the course of the learning process. Initially, one third of the karma is designated to the teacher and the other two thirds in equal parts to the students (and other stakeholders). While the teacher’s karma remains constant, the students’ karma can increase through virtuous actions. A virtuous action is a behavior that increases the intrinsic value of the class in view of the primary objective of the game, which is the effective and homogeneous transfer of knowledge. For example, a student might investigate a new topic and expose it to the entire class. This increases everyone’s knowledge, including that of the teacher.
The notion of karma is important because it allows the teacher to measure class performance according to whether an instance of the game achieved its goal. Assuming that a student’s karma is a proxy for participation in the class, and that the latter is positively correlated with the knowledge acquired by the student during that class, it holds that the aim of the game — the homogeneous dissemination of knowledge — is achieved when:
1. The average of the students’ karma is large compared to the initial average; and
2. The standard deviation of students’ karma is low (close to zero).
We thus define a Learning Return Index of the class as the ratio of the increase in average karma (with respect to the initial stage) and the standard deviation of the karma. The higher the index, the more the learning game has achieved its goal. A good class is one in which everyone has learned a lot. Situations to avoid are classes in which everyone has learned little, or a few have learned a lot and many have acquired little.
How can students use their karma?
They can use it to govern the class. As in a DAO, stakeholders of the learning game are called upon to make decisions through proposals, discussions, and voting. Decisions, however, are made within a scaffold decided in advance by the teacher through an approach of guided self-organization. For instance, students can participate in determining the course syllabus based on a choice of alternative topics. Or they can invite an external expert to give a lesson on a different topic of interest.
What is the voting mechanism?
Although there are many alternatives, we advocate the use of Quadratic Voting (QV), also known as Plural Voting. QV offers a more equitable distribution of voting power, wider voting participation, and greater diversification of votes. It has been used effectively in Gitcoin — the biggest funding platform for Web3 projects.
Quadratic Voting has many advantages over linear voting, one of which is that the power of big karma holders is radically scaled down while that of small karma holders is amplified.
For our purposes, a student’s karma might partially determine their final assessment, while that student’s peers might also contribute to their evaluation using their own karma power. Both self-evaluation and peer evaluation are central to the learning game, which is another way for students and other stakeholders to participate in the governance of the class.
Karmic remuneration is another means of utilizing acquired karma. At the end of a class, karma is translated into rewards, which might be in-game assets — access to premium internships or one-to-one meetings with domain experts — or else more liquid assets with potential economic value such as fungible and non-fungible tokens (e.g. digital art, digital land, or indeed avatar wearables). It is important to stress that the converting function between karma and rewards is influenced by the performance of the group, scored as an overall Learning Return Index for the class.
The higher the class performance as a whole, the higher the potential individual rewards. This creates a virtuous cycle whereby a single individual contributes through virtuous behavior to the growth of class-wide knowledge, while the learning performance of the whole in turn determines individual rewards.
As in any other DAO, the DAE class has a treasury. This is a fund with assets of economic value to be spent, through collective decisions, to achieve the aim of the game. One approach might be to invite an external expert for a lesson on a specific topic. Karmic rewards are also withdrawn from this fund.
This learning model has the invisible goal of instilling freedom and responsibility — two sides of the same coin — in all participants, both teachers and learners. It seeks to experiment with the concept of meritocracy and the sense of belonging to a community in which every individual is valuable and where their individual actions reflect on the whole community. In this sense, DAE is as much a political project as blockchain technology itself. DAE leverages ten principles of Web3:
1. Participation
2. Responsibility
3. Meritocracy
4. Inclusiveness
5. Autonomy
6. Gaming
7. Cubic learning
8. Scalability
9. Composability
10. Transparency
The DAE model takes full advantage of a number of Web3 applications:
As an associate professor of Computer Science, I have taken a sabbatical year, beginning in October 2022, to devise, organize, and implement this project. To support its work, DAE recently minted an art collection on the Tezos blockchain. These works are the product of ten different artists who were invited to represent, in their own artistic styles, the aforementioned ten principles of Web3. DAE is also due to curate a Space on SuperRare called Gallery/Library, which is a place to explore Web3 concepts through art — to learn and enjoy beauty at the same time. It is also a science and art academy. DAE is documented on GitBook.
To draft a fitting conclusion, I asked ChatGPT how Web3 technologies might be leveraged to improve the education system. The answer is very much aligned with the aims of DAE:
Web3 technologies, such as blockchain, decentralized networks, and smart contracts, have the potential to revolutionize the educational system by providing new ways of storing, sharing, and verifying educational data and credentials.
For example, blockchain technology could be used to create secure and transparent systems for storing and verifying educational qualifications, such as degrees and certifications. This could help to reduce the incidence of fraud and ensure that employers and institutions have confidence in the authenticity of an individual’s credentials.
Decentralized networks could also be used to create open and collaborative learning environments that allow students and educators from around the world to connect and share knowledge. Smart contracts could be used to automate the tracking of student progress, the issuance of credits and grades, and the management of educational resources.
Overall, Web3 technologies have the potential to make the educational system more efficient, transparent, and accessible, and to empower learners to take control of their own learning and career paths.
hex6c is a generative and crypto artist whose digital works are available as NFTs on SuperRare, KnownOrigin, Art Blocks, Hic et Nunc, and more. He is also a data scientist and Associate Professor of Computer Science at the Department of Mathematics, Computer Science and Physics, University of Udine. He has a PhD in Computer Science from the University of Udine and carried out postdoctoral research in logic at the University of Amsterdam.
